
"""simple docstring"""






import datasets


UpperCAmelCase        =				"""\
@InProceedings{conneau2018xnli,
  author = \"Conneau, Alexis
                 and Rinott, Ruty
                 and Lample, Guillaume
                 and Williams, Adina
                 and Bowman, Samuel R.
                 and Schwenk, Holger
                 and Stoyanov, Veselin\",
  title = \"XNLI: Evaluating Cross-lingual Sentence Representations\",
  booktitle = \"Proceedings of the 2018 Conference on Empirical Methods
               in Natural Language Processing\",
  year = \"2018\",
  publisher = \"Association for Computational Linguistics\",
  location = \"Brussels, Belgium\",
}
"""

UpperCAmelCase        =				"""\
XNLI is a subset of a few thousand examples from MNLI which has been translated
into a 14 different languages (some low-ish resource). As with MNLI, the goal is
to predict textual entailment (does sentence A imply/contradict/neither sentence
B) and is a classification task (given two sentences, predict one of three
labels).
"""

UpperCAmelCase        =				"""
Computes XNLI score which is just simple accuracy.
Args:
    predictions: Predicted labels.
    references: Ground truth labels.
Returns:
    'accuracy': accuracy
Examples:

    >>> predictions = [0, 1]
    >>> references = [0, 1]
    >>> xnli_metric = datasets.load_metric(\"xnli\")
    >>> results = xnli_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0}
"""




def __magic_name__	(				_lowerCamelCase:		Union[str, Any],					_lowerCamelCase:		Optional[int]			)					->						Any:

	'''simple docstring'''




	return (preds == labels).mean()



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION   ,_KWARGS_DESCRIPTION		)
class 			lowercase					( datasets.Metric		):






			def 					UpperCAmelCase							(self  :	int			)       ->	List[str]:





				"""simple docstring"""


				return datasets.MetricInfo(
				    description=_DESCRIPTION      ,citation=_CITATION      ,inputs_description=_KWARGS_DESCRIPTION      ,features=datasets.Features(
				        {
				            '''predictions''': datasets.Value('''int64''' if self.config_name != '''sts-b''' else '''float32'''			),
				            '''references''': datasets.Value('''int64''' if self.config_name != '''sts-b''' else '''float32'''			),
				        }			)      ,codebase_urls=[]      ,reference_urls=[]      ,format='''numpy'''      ,)






			def 					UpperCAmelCase							(self  :	Tuple      ,SCREAMING_SNAKE_CASE_  :	Optional[Any]      ,SCREAMING_SNAKE_CASE_  :	Tuple			)       ->	str:





				"""simple docstring"""


				return {"accuracy": simple_accuracy(SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_			)}




"""simple docstring"""






from itertools import count




def __magic_name__	(				_lowerCamelCase:		int = 50			)					->						int:

	'''simple docstring'''




	lowerCAmelCase												=  [1] * min_block_length

	for n in count(_lowerCamelCase			):
		fill_count_functions.append(1			)

		for block_length in range(_lowerCamelCase,					n + 1			):
			for block_start in range(n - block_length			):
				fill_count_functions[n] += fill_count_functions[
				    n - block_start - block_length - 1
				]

			fill_count_functions[n] += 1

		if fill_count_functions[n] > 1_000_000:
			break

	return n


if __name__ == "__main__":
					print(f"""{solution() = }""")


